CN114469037B - Heart rate measuring method based on millimeter wave radar - Google Patents
Heart rate measuring method based on millimeter wave radar Download PDFInfo
- Publication number
- CN114469037B CN114469037B CN202210111994.9A CN202210111994A CN114469037B CN 114469037 B CN114469037 B CN 114469037B CN 202210111994 A CN202210111994 A CN 202210111994A CN 114469037 B CN114469037 B CN 114469037B
- Authority
- CN
- China
- Prior art keywords
- vector data
- line vector
- radar
- signal line
- phase information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 239000013598 vector Substances 0.000 claims abstract description 208
- 238000001228 spectrum Methods 0.000 claims abstract description 27
- 238000009432 framing Methods 0.000 claims abstract description 21
- 230000005484 gravity Effects 0.000 claims abstract description 17
- 238000009532 heart rate measurement Methods 0.000 claims abstract description 15
- 238000006243 chemical reaction Methods 0.000 claims abstract description 13
- 230000000737 periodic effect Effects 0.000 claims abstract description 10
- 238000009499 grossing Methods 0.000 claims abstract description 8
- 238000010606 normalization Methods 0.000 claims description 30
- 238000013139 quantization Methods 0.000 claims description 15
- 238000000354 decomposition reaction Methods 0.000 claims description 10
- 238000000605 extraction Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000003672 processing method Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 12
- 238000001514 detection method Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 description 8
- 239000002131 composite material Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000001931 thermography Methods 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/0507—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves using microwaves or terahertz waves
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Public Health (AREA)
- Animal Behavior & Ethology (AREA)
- Physics & Mathematics (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Cardiology (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Radar Systems Or Details Thereof (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention discloses a high-reliability heart rate measurement method based on millimeter wave radar. The method comprises the steps of normalizing radar row vector data of a plurality of groups of distance units acquired by millimeter wave radar and calculating accumulated energy to obtain the radar row vector data with the largest accumulated energy; extracting phase information of row vector data with maximum accumulated energy by using an inverse tangent method; unwrapping is carried out according to the phase information line vector, and vibration signal line vector is obtained through first-order differential processing and distance conversion; MAD smoothing and wavelet denoising are carried out on the vibration signal line vector, and a heartbeat signal line vector is obtained after the vibration signal line vector passes through a band-pass filter; and calculating an average periodic chart power spectrum after the framing windowing pretreatment is performed on the heartbeat signal line vector, and taking the gravity center frequency of the power spectrum as a measurement result of the heart rate of the human body. The advantages are that: compared with the prior art, the method improves the measurement accuracy, has strong anti-interference capability, and avoids the problems of large fluctuation of measurement results and low reliability caused by high-frequency noise and the peak detection method in the prior art.
Description
Technical Field
The invention belongs to the technical field of millimeter wave radar data processing, and particularly relates to a heart rate measurement method based on millimeter wave radar.
Background
Heart rate measurement is an important index for human vital sign detection, and the main method for measuring human heart rate is contact measurement in the medical field at present, but because the contact measurement needs to enable equipment to be in contact with a human body for measurement, the preparation work is complex, the operation steps are complex, and the method is not suitable for occasions such as rescue, detection and the like. In recent years, a number of non-contact heart rate measurement technologies have emerged, mainly based on video imaging technology, radar technology, thermal imaging technology, etc. Among them, radar technology is widely used in heart rate measurement in many occasions due to its ability to penetrate clothing and obstacles.
The radar technology currently applied to the heart rate measurement field is mainly millimeter wave biological radar. The heartbeat can cause the vibration of the chest of the human body, and the vibration information of the chest of the human body is measured by the radar technology, so that the heartbeat information can be extracted. At present, a high-precision millimeter wave radar capable of meeting the requirement of measuring the chest micro-vibration has been designed, the radar structure is gradually simplified, and the portability is greatly improved.
In recent years, in the application field of millimeter wave biological radars, the method has been realized that vital sign signals are extracted from a composite wave of a transmitted wave and a reflected wave, heartbeat signals are separated, heart rate is estimated through methods such as short-time Fourier transform, peak detection method and the like, and non-contact measurement of human heart rate under normal conditions can be realized. In practical application, due to the influence of environmental noise, the heart rate measurement result has large error and serious fluctuation of data, and the measurement result has low reliability.
The invention comprises the following steps:
in order to solve the problems of large measurement errors and large fluctuation of measurement data caused by medium-high frequency noise when the millimeter wave radar measures heart rate, the invention provides a heart rate measurement method based on the millimeter wave radar based on a wavelet noise reduction technology and a power spectrum center-of-gravity frequency estimation method.
The technical scheme of the invention is a heart rate measurement method based on millimeter wave radar, which is characterized by comprising the following steps:
step 1, acquiring radar signal line vector data of K groups of distance units through millimeter wave radar, wherein the dimension of the radar signal line vector data of each group of distance units is N, carrying out normalization processing on the radar signal line vector data of each group of distance units to obtain radar signal line vector data of each group of distance units after normalization processing, and calculating the accumulated energy of the radar signal line vector data of each group of distance units after normalization processing; selecting radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy from the accumulated energy of the radar signal line vector data of the K groups of normalized distance units, taking the radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy as the radar data of the part where the human body is located, and further processing the radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy by an inverse tangent method phase signal extraction method to obtain radar phase information line vector data;
step 2, the radar phase information line vector data is subjected to phase unwrapping processing to obtain unwrapped phase information line vector data;
step 3, performing first-order difference processing on the unwrapped phase information row vector data to obtain unwrapped phase row direction difference row vector data, and performing distance conversion on the unwrapped phase row direction difference row vector data to obtain distance converted vibration signal row vector data;
step 4, smoothing the vibration signal line vector data after the distance conversion by using an MAD smoothing method to obtain vibration signal line vector data with outliers removed;
step 5, performing wavelet noise reduction processing on the vibration signal line vector data from which the outliers are removed to obtain wavelet noise reduced vibration signal line vector data;
step 6, filtering the vibration signal line vector data after wavelet noise reduction through a band-pass filter to obtain heartbeat signal line vector data;
step 7, firstly carrying out framing and windowing on the heart beat signal row vector data to obtain a windowed heart beat signal, calculating a power spectrum of an average periodic chart according to the power spectrum of the heart beat signal after windowing, calculating a gravity center frequency according to the power spectrum of the average periodic chart, and taking the gravity center frequency as a human body estimated heart rate;
preferably, in step 1, the accumulated energy of the radar signal line vector data of each group of distance units after normalization processing is calculated, and the specific method is as follows:
wherein x is k (N) represents the nth radar data in the radar signal line vector data of the kth group of normalized distance units, N is the line vector data dimension of the radar signal line vector data of each group of normalized distance units, and E (k) is the accumulated energy of the radar signal line vector data of the kth group of normalized distance units;
the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy in the step 1 is as follows:
x c (n),n∈[1,N]
wherein x is c (n) n-th radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, namely n-th radar data in the radar signal line vector data of the distance unit after normalization processing of the c-th group, (x) c (1),x c (2),...,x c (N)) represents the number of radar signal line vectors of the normalized distance cell corresponding to the largest accumulated energy, N being the line vector data dimension of the radar signal line vector data of the normalized distance cell corresponding to the largest accumulated energy;
the processing method for extracting phase information by the arctangent method in the step 1 comprises the following specific steps:
wherein I is c (n) the imaginary part of the nth radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, R c (N) the real part of the nth radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, N being the line vector data dimension of the radar phase information line vector data, phi c (n) represents the nth radar phase information in the radar phase information row vector data, (phi) c (1),φ c (2),...,φ c (N)) represents radar phase information line vector data;
preferably, in step 2, the phase information line vector data after being unwound is obtained through a phase unwinding process, specifically:
wherein phi is c (n) represents nth radar phase information in the radar phase information row vector data,
(φ c (1),φ c (2),...,φ c (N)) represents radar phase information line vector data,represents the nth unwrapped phase information in the unwrapped phase information row vector data,/>Representing unwrapped phase information line vector data, N being the line vector data dimension of the unwrapped phase information line vector data;
preferably, the step 3 of performing the first-order difference processing to obtain unwrapped phase row direction difference row vector data specifically includes:
wherein,representing the nth unwrapped phase information in the unwrapped phase information row vector data, N being the row vector data dimension, Δφ, of the unwrapped phase row direction difference row vector data c (n) the phase difference of the nth phase data in the unwrapped phase row-wise difference vector data,
phase line direction difference line vector data after being unwound;
and step 3, performing distance conversion to obtain vibration signal row vector data after the distance conversion, wherein the vibration signal row vector data specifically comprises the following steps:
wherein lambda is the wavelength of linear frequency modulation wave emitted by millimeter wave radar, and Deltax c (n) is the nth distance-converted vibration signal in the distance-converted vibration signal line vector data, (Δx) c (1),Δx c (2),...,Δx c (N)) represents distance-converted vibration signal line vector data, N represents a line vector data dimension of the distance-converted vibration signal line vector data;
preferably, in step 5, the wavelet noise reduction processing of the outlier-removed vibration signal line vector data is:
sequentially performing wavelet decomposition, threshold quantization processing and signal reconstruction on the vibration signal line vector data from which outliers are removed;
the wavelet decomposition is to perform Q-layer decomposition on vibration signal line vector data from which outliers are removed, so as to obtain a decomposed wavelet signal;
the threshold quantization processing is to perform threshold quantization processing on the high-frequency coefficient of the decomposed wavelet signal in a soft threshold processing mode to obtain the wavelet signal after the threshold quantization processing;
the signal reconstruction is to reconstruct the wavelet signal after the threshold quantization processing to obtain the vibration signal line vector data after wavelet noise reduction.
Preferably, the band-pass filter in the step 6 is an IIR band-pass filter or an FIR band-pass filter, and the band-pass frequency is 0.8Hz-2Hz;
preferably, in step 7, the heartbeat signal line vector data is first framed and windowed to obtain a windowed heartbeat signal, where the windowed heartbeat signal is:
dividing the heartbeat signal line vector data into heartbeat signals after M frames are divided into frames, wherein the frame length of each frame of heartbeat signal after the frame division is L;
the heartbeat signal after framing is expressed as:
x ce ((m-1)*L+i),m∈[1,M],i∈[1,L]
wherein x is ce ((m-1) l+i) represents the ith heartbeat signal in the heartbeat signals after framing the mth frame, namely the (m-1) l+i heartbeat signal in the heartbeat signal line vector data;
and 7, the framing and windowing are as follows:
x ce1 (m*L+i)＝x ce (m*L+i)F w (m*L+i),m∈[1,L],i∈[1,M]
wherein x is ce (m*L+i),i∈[1,M]For the heartbeat signal after the m-th frame is divided into frames, F w (m x L+i) is a window function of the heartbeat signal after the mth frame is divided into frames, x ce1 (m*L+i),i∈[1,M]The heartbeat signal after framing and windowing of the mth frame;
step 7, calculating an average periodic chart power spectrum according to the power spectrum of the heartbeat signal after windowing, specifically:
wherein,is a normalization factor, F w (n) is a window function,ω g ,g∈[1,G]for the G-th discrete angular frequency, G represents the number of discrete angular frequencies, M is the number of frames of the heartbeat signal, L is the frame length of each frame, and x ce1 (m+L+n) is the nth heartbeat signal in the heartbeat signals after framing and windowing of the mth frame,/th frame>For the g-th discrete angular frequency omega g Is a power spectrum value of (a);
and 7, calculating the center of gravity frequency through the power spectrum of the heartbeat signal, wherein the center of gravity frequency is specifically:
wherein omega g For the g-th discrete angular frequency,to correspond to the g-th discrete angular frequency omega g Power spectrum value of->A centre of gravity frequency;
the invention solves the problems of large heart rate measurement error and large fluctuation of heart rate measurement results caused by middle and high frequency noise in the existing heart rate measurement method.
According to the invention, the data smoothing processing based on the MAD method is performed on the vibration signals acquired by the millimeter wave radar, so that coarse errors amplified by the first-order differential processing are eliminated, and the influence of outliers in the measured data on subsequent signal processing is avoided.
According to the invention, wavelet noise reduction processing is carried out on the vibration signals acquired by the millimeter wave radar, so that medium-high frequency noise in the acquired signals is well inhibited, the heart rate measurement precision and anti-interference performance are greatly improved, and the Q-layer wavelet decomposition is carried out on the signals, so that the calculated amount of a data processing module is reduced, meanwhile, a good noise reduction effect can be obtained, and the local jitter phenomenon of the noise reduction result is avoided by adopting a soft threshold processing mode.
According to the invention, the power spectrum estimation is carried out on the heartbeat signal acquired by the millimeter wave radar, and the average heart rate in one measurement period can be better estimated by adopting the gravity center frequency estimation method, so that the stability and reliability of the measurement result are improved.
Description of the drawings:
fig. 1: is a flow chart of the method of the invention;
the specific embodiment is as follows:
in order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
The heart rate measurement method based on the millimeter wave radar is characterized by comprising the following steps of:
step 1, acquiring radar signal line vector data of K=19 groups of distance units through a millimeter wave radar, wherein the dimension of the radar signal line vector data of each group of distance units is N=2048, carrying out normalization processing on the radar signal line vector data of each group of distance units to obtain radar signal line vector data of each group of distance units after normalization processing, and calculating the accumulated energy of the radar signal line vector data of each group of distance units after normalization processing; selecting radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy from the accumulated energy of the radar signal line vector data of the K groups of normalized distance units, taking the radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy as the radar data of the part where the human body is located, and further processing the radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy by an inverse tangent method phase signal extraction method to obtain radar phase information line vector data;
the step 1 is to calculate the accumulated energy of the radar signal row vector data of each group of distance units after normalization processing, and the specific method is as follows:
wherein x is k (N) represents the nth radar data in the radar signal line vector data of the kth group of normalized distance units, n=2048 is the line vector data dimension of the radar signal line vector data of each group of normalized distance units, and E (k) is the accumulated energy of the radar signal line vector data of the kth group of normalized distance units;
the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy in the step 1 is as follows:
x c (n),n∈[1,N]
wherein x is c (n) n-th radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, namely n-th radar data in the radar signal line vector data of the distance unit after normalization processing of the c-th group, (x) c (1),x c (2),...,x c (N)) represents the number of radar signal line vectors of the normalized distance cell corresponding to the largest accumulated energy, N being the line vector data dimension of the radar signal line vector data of the normalized distance cell corresponding to the largest accumulated energy;
the processing method for extracting phase information by the arctangent method in the step 1 comprises the following specific steps:
wherein I is c (n) the imaginary part of the nth radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, R c (N) the real part of the nth radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, N being the radar phase information line vector dataLine vector data dimension, phi c (n) represents the nth radar phase information in the radar phase information row vector data, (phi) c (1),φ c (2),...,φ c (N)) represents radar phase information line vector data;
step 2, the radar phase information line vector data is subjected to phase unwrapping processing to obtain unwrapped phase information line vector data;
step 2, obtaining unwrapped phase information row vector data through phase unwrapping processing, specifically:
wherein phi is c (n) represents nth radar phase information in the radar phase information row vector data,
(φ c (1),φ c (2),...,φ c (N)) represents radar phase information line vector data,represents the nth unwrapped phase information in the unwrapped phase information row vector data,/>Representing unwrapped phase information line vector data, N being the line vector data dimension of the unwrapped phase information line vector data;
step 3, performing first-order difference processing on the unwrapped phase information row vector data to obtain unwrapped phase row direction difference row vector data, and performing distance conversion on the unwrapped phase row direction difference row vector data to obtain distance converted vibration signal row vector data;
step 3, performing first-order differential processing to obtain unwrapped phase row direction differential row vector data, which specifically includes:
wherein,representing the nth unwrapped phase information in the unwrapped phase information row vector data, N being the row vector data dimension, Δφ, of the unwrapped phase row direction difference row vector data c (n) the phase difference of the nth phase data in the unwrapped phase row-wise difference vector data,
phase line direction difference line vector data after being unwound;
and step 3, performing distance conversion to obtain vibration signal row vector data after the distance conversion, wherein the vibration signal row vector data specifically comprises the following steps:
wherein lambda is the wavelength of linear frequency modulation wave emitted by millimeter wave radar, and Deltax c (n) is the nth distance-converted vibration signal in the distance-converted vibration signal line vector data, (Δx) c (1),Δx c (2),...,Δx c (N)) represents distance-converted vibration signal line vector data, N represents a line vector data dimension of the distance-converted vibration signal line vector data;
step 4, smoothing the vibration signal line vector data after the distance conversion by using an MAD smoothing method to obtain vibration signal line vector data with outliers removed;
step 5, performing wavelet noise reduction processing on the vibration signal line vector data from which the outliers are removed to obtain wavelet noise reduced vibration signal line vector data;
and 5, performing wavelet noise reduction processing on the vibration signal line vector data with outliers removed, wherein the wavelet noise reduction processing comprises the following steps of:
sequentially performing wavelet decomposition, threshold quantization processing and signal reconstruction on the vibration signal line vector data from which outliers are removed;
the wavelet decomposition is to perform Q=2-layer decomposition on vibration signal line vector data from which outliers are removed, so as to obtain a decomposed wavelet signal;
the threshold quantization processing is to perform threshold quantization processing on the high-frequency coefficient of the decomposed wavelet signal in a soft threshold processing mode to obtain the wavelet signal after the threshold quantization processing;
the signal reconstruction is to reconstruct the wavelet signal after the threshold quantization processing to obtain the vibration signal line vector data after wavelet noise reduction.
Step 6, filtering the vibration signal line vector data after wavelet noise reduction through a band-pass filter to obtain heartbeat signal line vector data;
the band-pass filter in the step 6 is an IIR band-pass filter or an FIR band-pass filter, and the band-pass frequency is 0.8Hz-2Hz;
step 7, firstly carrying out framing and windowing on the heart beat signal row vector data to obtain a windowed heart beat signal, calculating a power spectrum of an average periodic chart according to the power spectrum of the heart beat signal after windowing, calculating a gravity center frequency according to the power spectrum of the average periodic chart, and taking the gravity center frequency as a human body estimated heart rate;
step 7, firstly framing and windowing the heartbeat signal row vector data to obtain windowed heartbeat signals, wherein the windowed heartbeat signals are as follows:
dividing the heartbeat signal line vector data into heartbeat signals after M frames are divided into frames, wherein the frame length of each frame of heartbeat signal after the frame division is L;
the heartbeat signal after framing is expressed as:
x ce ((m-1)*L+i),m∈[1,M],i∈[1,L]
xce((m-1)*L+i)，m∈[1，M]，i∈[1，L]
wherein x is ce ((m-1) l+i) represents the ith heartbeat signal in the heartbeat signals after framing the mth frame, namely the (m-1) l+i heartbeat signal in the heartbeat signal line vector data;
and 7, the framing and windowing are as follows:
x ce1 (m*L+i)＝x ce (m*L+i)F w (m*L+i),m∈[1,L],i∈[1,M]
wherein x is ce (m*L+i),i∈[1,M]For the heartbeat signal after the m-th frame is divided into frames, F w (m x L+i) is a window function of the heartbeat signal after the mth frame is divided into frames, x ce1 (m*L+i),i∈[1,M]The heartbeat signal after framing and windowing of the mth frame;
step 7, calculating an average periodic chart power spectrum according to the power spectrum of the heartbeat signal after windowing, specifically:
wherein,is a normalization factor, F w (n) is a Hamming window function, ω g ,g∈[1,G]For the G-th discrete angular frequency, G represents the number of discrete angular frequencies, M is the number of frames of the heartbeat signal, L is the frame length of each frame, and x ce1 (m+L+n) is the nth heartbeat signal in the heartbeat signals after framing and windowing of the mth frame,/th frame>For the g-th discrete angular frequency omega g Is a power spectrum value of (a);
and 7, calculating the center of gravity frequency through the power spectrum of the heartbeat signal, wherein the center of gravity frequency is specifically:
wherein omega g For the g-th discrete angular frequency,to correspond to the g-th discrete angular frequency omega g Power spectrum value of->Is the center of gravity frequency.
It should be understood that parts of the specification not specifically set forth herein are all prior art.
It should be understood that the foregoing description of the embodiments is not intended to limit the scope of the invention, but rather to make substitutions and modifications within the scope of the invention as defined by the appended claims without departing from the scope of the invention.
Claims (1)
1. The heart rate measurement method based on the millimeter wave radar is characterized by comprising the following steps of:
step 1, acquiring radar signal line vector data of K groups of distance units through millimeter wave radar, wherein the dimension of the radar signal line vector data of each group of distance units is N, carrying out normalization processing on the radar signal line vector data of each group of distance units to obtain radar signal line vector data of each group of distance units after normalization processing, and calculating the accumulated energy of the radar signal line vector data of each group of distance units after normalization processing; selecting radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy from the accumulated energy of the radar signal line vector data of the K groups of normalized distance units, taking the radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy as the radar data of the part where the human body is located, and further processing the radar signal line vector data of the normalized distance unit corresponding to the largest accumulated energy by an inverse tangent method phase signal extraction method to obtain radar phase information line vector data;
step 2, the radar phase information line vector data is subjected to phase unwrapping processing to obtain unwrapped phase information line vector data;
step 3, performing first-order difference processing on the unwrapped phase information row vector data to obtain unwrapped phase row direction difference row vector data, and performing distance conversion on the unwrapped phase row direction difference row vector data to obtain distance converted vibration signal row vector data;
step 4, smoothing the vibration signal line vector data after the distance conversion by using an MAD smoothing method to obtain vibration signal line vector data with outliers removed;
step 5, performing wavelet noise reduction processing on the vibration signal line vector data from which the outliers are removed to obtain wavelet noise reduced vibration signal line vector data;
step 6, filtering the vibration signal line vector data after wavelet noise reduction through a band-pass filter to obtain heartbeat signal line vector data;
step 7, firstly carrying out framing and windowing on the heart beat signal row vector data to obtain a windowed heart beat signal, calculating a power spectrum of an average periodic chart according to the power spectrum of the heart beat signal after windowing, calculating a gravity center frequency according to the power spectrum of the average periodic chart, and taking the gravity center frequency as a human body estimated heart rate;
the step 1 is to calculate the accumulated energy of the radar signal row vector data of each group of distance units after normalization processing, and the specific method is as follows:
wherein x is k (N) represents the nth radar data in the radar signal line vector data of the kth group of normalized distance units, N is the line vector data dimension of the radar signal line vector data of each group of normalized distance units, and E (k) is the accumulated energy of the radar signal line vector data of the kth group of normalized distance units;
the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy in the step 1 is as follows:
x c (n),n∈[1,N]
wherein x is c (n) n-th radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, namely n-th radar data in the radar signal line vector data of the distance unit after normalization processing of the c-th group, (x) c (1),x c (2),...,x c (N)) represents the number of radar signal line vectors of the normalized distance cell corresponding to the largest accumulated energy, N being the line vector data dimension of the radar signal line vector data of the normalized distance cell corresponding to the largest accumulated energy;
the processing method for extracting phase information by the inverse tangent method in the step 1 comprises the following specific steps:
wherein I is c (n) the imaginary part of the nth radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, R c (N) the real part of the nth radar data in the radar signal line vector data of the distance unit after normalization processing corresponding to the maximum accumulated energy, N being the line vector data dimension of the radar phase information line vector data, phi c (n) represents the nth radar phase information in the radar phase information row vector data, (phi) c (1),φ c (2),...,φ c (N)) represents radar phase information line vector data;
step 2, obtaining unwrapped phase information row vector data through phase unwrapping processing, specifically:
wherein phi is c (n) represents nth radar phase information in the radar phase information row vector data,
(φ c (1),φ c (2),...,φ c (N)) represents radar phase information line vector data,represents the nth unwrapped phase information in the unwrapped phase information row vector data,/>Representing unwrapped phase information line vector data, N being the line vector data dimension of the unwrapped phase information line vector data;
step 3, performing first-order differential processing to obtain unwrapped phase row direction differential row vector data, which specifically includes:
wherein,representing the nth unwrapped phase information in the unwrapped phase information row vector data, N being the row vector data dimension, Δφ, of the unwrapped phase row direction difference row vector data c (n) is the phase difference of the nth phase data in the unwrapped phase-line direction difference vector data, ">The n-1 th unwrapped phase information in the unwrapped phase information row vector data; />Phase line direction difference line vector data after being unwound;
and step 3, performing distance conversion to obtain vibration signal row vector data after the distance conversion, wherein the vibration signal row vector data specifically comprises the following steps:
wherein lambda is the wavelength of linear frequency modulation wave emitted by millimeter wave radar, and Deltax c (n) is the nth distance-converted vibration signal in the distance-converted vibration signal line vector data, (Δx) c (1),Δx c (2),...,Δx c (N)) represents distanceThe converted vibration signal row vector data, N represents the row vector data dimension of the distance converted vibration signal row vector data;
and 5, performing wavelet noise reduction processing on the vibration signal line vector data with outliers removed, wherein the wavelet noise reduction processing comprises the following steps of:
sequentially performing wavelet decomposition, threshold quantization processing and signal reconstruction on the vibration signal line vector data from which outliers are removed;
the wavelet decomposition is to perform Q-layer decomposition on vibration signal line vector data from which outliers are removed, so as to obtain a decomposed wavelet signal;
the threshold quantization processing is to perform threshold quantization processing on the high-frequency coefficient of the decomposed wavelet signal in a soft threshold processing mode to obtain the wavelet signal after the threshold quantization processing;
the signal reconstruction is to reconstruct a wavelet signal after threshold quantization processing to obtain vibration signal line vector data after wavelet noise reduction;
the band-pass filter in the step 6 is an IIR band-pass filter or an FIR band-pass filter, and the band-pass frequency is 0.8Hz-2Hz;
step 7, firstly framing and windowing the heartbeat signal row vector data to obtain windowed heartbeat signals, wherein the windowed heartbeat signals are as follows:
dividing the heartbeat signal line vector data into heartbeat signals after M frames are divided into frames, wherein the frame length of each frame of heartbeat signal after the frame division is L;
the heartbeat signal after framing is expressed as:
x ce ((m-1)*L+i),m∈[1,M],i∈[1,L]
wherein x is ce ((m-1) l+i) represents an i-th heartbeat signal in the heartbeat signals after framing the mth frame, namely (m-1) l+i-th heartbeat signal in the heartbeat signal line vector data, and represents a multiplication symbol;
and 7, framing and windowing are as follows:
x ce1 (m*L+i)＝x ce (m*L+i)F w (m*L+i),
wherein x is ce (m+L+i) is the ith heartbeat signal in the heartbeat signals after the m+1 th frame is divided into frames, F w (m+L+i) is a window function of the heartbeat signal after the m+1st frame is divided into frames, x ce1 (m+l+i) representing a multiplication symbol, which is the i-th heartbeat signal in the heartbeat signals after framing and windowing of the m+1-th frame;
step 7, calculating an average periodic chart power spectrum according to the power spectrum of the heartbeat signal after windowing, specifically:
wherein,is a normalization factor, F w (p) is a window function, ω g ,g∈[1,G]For the G-th discrete angular frequency, G represents the number of discrete angular frequencies, M is the number of frames of the heartbeat signal, L is the frame length of each frame, and x ce1 (m+L+p) is the p-th heartbeat signal in the heartbeat signals after framing and windowing of the m+1-th frame,/h>For the g-th discrete angular frequency omega g Power spectrum values of (a) representing multiplication symbols;
and 7, calculating the center of gravity frequency through the power spectrum of the heartbeat signal, wherein the center of gravity frequency is specifically:
wherein omega g For the g-th discrete angular frequency,to correspond to the g-th discrete angular frequency omega g Power spectrum value of->Is the center of gravity frequency.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210111994.9A CN114469037B (en) | 2022-01-29 | 2022-01-29 | Heart rate measuring method based on millimeter wave radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210111994.9A CN114469037B (en) | 2022-01-29 | 2022-01-29 | Heart rate measuring method based on millimeter wave radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114469037A CN114469037A (en) | 2022-05-13 |
| CN114469037B true CN114469037B (en) | 2024-01-12 |
Family
ID=81477998
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210111994.9A Active CN114469037B (en) | 2022-01-29 | 2022-01-29 | Heart rate measuring method based on millimeter wave radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114469037B (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08275934A (en) * | 1995-04-04 | 1996-10-22 | Agency Of Ind Science & Technol | Correlation investigating system |
| JP2010264095A (en) * | 2009-05-15 | 2010-11-25 | Nissan Motor Co Ltd | Heart rate measuring apparatus and heart rate measuring method |
| CN102499664A (en) * | 2011-10-24 | 2012-06-20 | 西双版纳大渡云海生物科技发展有限公司 | Video-image-based method and system for detecting non-contact vital sign |
| CN103536282A (en) * | 2013-11-06 | 2014-01-29 | 中国人民解放军第三军医大学 | Magnetic induction cardiopulmonary activity signal separation method based on Fast-ICA method |
| CN108158573A (en) * | 2017-12-26 | 2018-06-15 | 智慧康源（厦门）科技有限公司 | Electrocardiosignal noise-reduction method based on adaptive threshold wavelet transformation |
| CN111419208A (en) * | 2020-04-13 | 2020-07-17 | 河北工业大学 | Acceleration sensor-based unbound real-time heart rate monitoring method and system |
| JP2020130745A (en) * | 2019-02-22 | 2020-08-31 | 株式会社豊田中央研究所 | Mind and body management system |
| CN111685741A (en) * | 2020-06-11 | 2020-09-22 | 中山大学 | Method for detecting human body respiration rate and heart rate based on orthogonal demodulation pulse ultra-wideband radar |
| CN111743524A (en) * | 2020-06-19 | 2020-10-09 | 联想(北京)有限公司 | Information processing method, terminal and computer readable storage medium |
| CN112438707A (en) * | 2019-08-16 | 2021-03-05 | 富士通株式会社 | Detection device, method and system for vital signs |
| CN112754441A (en) * | 2021-01-08 | 2021-05-07 | 杭州环木信息科技有限责任公司 | Millimeter wave-based non-contact heartbeat detection method |
| JP2021146061A (en) * | 2020-03-23 | 2021-09-27 | 株式会社リコー | Biological information acquisition device, biological information acquisition method and program |
| CN113729677A (en) * | 2021-10-12 | 2021-12-03 | 九州云合(山东)智能科技有限公司 | Intelligent vital sign monitoring method |
| CN113854990A (en) * | 2021-10-27 | 2021-12-31 | 青岛海信日立空调系统有限公司 | Heartbeat detection method and device |
| CN113854981A (en) * | 2021-09-24 | 2021-12-31 | 南京六季光电技术研究院有限公司 | Millimeter wave radar-based sports multi-person vital sign monitoring method |
-
2022
- 2022-01-29 CN CN202210111994.9A patent/CN114469037B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08275934A (en) * | 1995-04-04 | 1996-10-22 | Agency Of Ind Science & Technol | Correlation investigating system |
| JP2010264095A (en) * | 2009-05-15 | 2010-11-25 | Nissan Motor Co Ltd | Heart rate measuring apparatus and heart rate measuring method |
| CN102499664A (en) * | 2011-10-24 | 2012-06-20 | 西双版纳大渡云海生物科技发展有限公司 | Video-image-based method and system for detecting non-contact vital sign |
| CN103536282A (en) * | 2013-11-06 | 2014-01-29 | 中国人民解放军第三军医大学 | Magnetic induction cardiopulmonary activity signal separation method based on Fast-ICA method |
| CN108158573A (en) * | 2017-12-26 | 2018-06-15 | 智慧康源（厦门）科技有限公司 | Electrocardiosignal noise-reduction method based on adaptive threshold wavelet transformation |
| JP2020130745A (en) * | 2019-02-22 | 2020-08-31 | 株式会社豊田中央研究所 | Mind and body management system |
| CN112438707A (en) * | 2019-08-16 | 2021-03-05 | 富士通株式会社 | Detection device, method and system for vital signs |
| JP2021146061A (en) * | 2020-03-23 | 2021-09-27 | 株式会社リコー | Biological information acquisition device, biological information acquisition method and program |
| CN111419208A (en) * | 2020-04-13 | 2020-07-17 | 河北工业大学 | Acceleration sensor-based unbound real-time heart rate monitoring method and system |
| CN111685741A (en) * | 2020-06-11 | 2020-09-22 | 中山大学 | Method for detecting human body respiration rate and heart rate based on orthogonal demodulation pulse ultra-wideband radar |
| CN111743524A (en) * | 2020-06-19 | 2020-10-09 | 联想(北京)有限公司 | Information processing method, terminal and computer readable storage medium |
| CN112754441A (en) * | 2021-01-08 | 2021-05-07 | 杭州环木信息科技有限责任公司 | Millimeter wave-based non-contact heartbeat detection method |
| CN113854981A (en) * | 2021-09-24 | 2021-12-31 | 南京六季光电技术研究院有限公司 | Millimeter wave radar-based sports multi-person vital sign monitoring method |
| CN113729677A (en) * | 2021-10-12 | 2021-12-03 | 九州云合(山东)智能科技有限公司 | Intelligent vital sign monitoring method |
| CN113854990A (en) * | 2021-10-27 | 2021-12-31 | 青岛海信日立空调系统有限公司 | Heartbeat detection method and device |
Non-Patent Citations (5)
| Title |
|---|
| Doppler Radar System for Long Range Detection of Respiration and Heart Rate;Ook, Park Seong;The Journal Of Korean Institute of Electromagnetic Engineering and Science;第25卷(第4期);全文 * |
| Spectral characteristics of heart rate fluctuations during parabolic flight;Bart Verheyden, et al;European journal of applied physiology;第95卷;全文 * |
| 何正嘉，张西宁.现代信号处理及工程应用.西安：西安交通大学出版社,2007,210-212. * |
| 基于视频分析的非接触式心率检测系统研究;陈怡然;中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑)(第5期);全文 * |
| 王展.现代数字信号处理.长沙：国防科技大学出版社 ,2016,212-218. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114469037A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109965858B (en) | Ultra-wideband radar-based human body vital sign detection method and device | |
| CN110208785B (en) | Radar maneuvering target rapid detection method based on robust sparse fractional Fourier transform | |
| CN108645620B (en) | Rolling bearing early fault diagnosis method based on information entropy and multi-scale morphology | |
| Aboy et al. | Adaptive modeling and spectral estimation of nonstationary biomedical signals based on Kalman filtering | |
| CN115644840A (en) | Vital sign detection method based on millimeter wave radar | |
| Yamamoto et al. | Non-contact heartbeat detection by heartbeat signal reconstruction based on spectrogram analysis with convolutional LSTM | |
| CN108053842A (en) | Shortwave sound end detecting method based on image identification | |
| Tang et al. | ECG de-noising based on empirical mode decomposition | |
| CN116153329A (en) | CWT-LBP-based sound signal time-frequency texture feature extraction method | |
| RU2367970C2 (en) | Device for detection of narrow-band noise hydroacoustic signals based on calculation of integral wavelet-spectrum | |
| CN117838083A (en) | Millimeter wave radar-based physical sign rapid and accurate detection method | |
| CN114469037B (en) | Heart rate measuring method based on millimeter wave radar | |
| CN110420022B (en) | P wave detection method based on dual-density wavelet transform | |
| CN110742593B (en) | Life signal feature extraction method based on line spectrum tracking | |
| CN116058818A (en) | Ultra-wideband radar heart rate detection method based on multi-sequence WOA-VMD algorithm | |
| CN102783945A (en) | Fetal electrocardiogram signal extracting method based on wavelet threshold denoising | |
| CN114820847A (en) | Amplitude extraction method for transmission attenuation ultrasonic tomography | |
| CN113397524A (en) | Respiration detection method, device, equipment and storage medium | |
| RU2464588C1 (en) | Apparatus for detecting hydroacoustic noise signals in form of sequence of sounds based on calculating integral wavelet spectrum | |
| CN113095113B (en) | Wavelet line spectrum feature extraction method and system for underwater target identification | |
| Kang et al. | Pulse Rate Estimation using PPG Affected with Motion Artifacts Base d on VMD and Hilbert Transform | |
| Li et al. | A Novel Heart Rate Estimation Method Exploiting Heartbeat Second Harmonic Reconstruction Via Millimeter Wave Radar | |
| CN113189570B (en) | Array signal processing method and system based on complex domain compressed sensing | |
| Xiang et al. | Heart rate estimation algorithm for radar signals based on wavelet threshold noise reduction and Resnet50 | |
| Xu et al. | Research on Heart Sound Denoising Method Based on CEEMDAN and Optimal Wavelet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |